The problem is the industry obsession on concatenating messages into a conversation stream. There is no reason to do it this way. Every time you run inference on the model, the client gets to compose the context in any way they want; there are more things than just concatenating prompts and LLM ouputs. (A drawback is caching won't help much if most of the context window is composed dynamically)
Coding CLIs as well as web chat works well because the agent can pull in information into the session at will (read a file, web search). The pain point is that if you're appending messages a stream, you're just slowly filling up the context.
The fix is to keep the message stream concept for informal communication with the prompter, but have an external, persistent message system that the agent can interact with (a bit like email). The agent can decide which messages they want to pull into the context, and which ones are no longer relevant.
The key is to give the agent not just the ability to pull things into context, but also remove from it. That gives you the eternal context needed for permanent, daemonized agents.
The problem is that the models are not trained for this, nor for any other non-standard agentic approach. It's like fighting their 'instincts' at every step, and the results I've been getting were not great.
Fwiw, I was playing with an "outliner"-tool collapse/expand idiom, on synthetic literate-programming markdown files, with #ids on headers and blocks. Insufficient experience to suggest it works, but it wasn't obviously not working, and that with a non-frontier model and very little guidance. Other familiar related idioms include <details>/<summary>, hierarchical breadcrumbs, and plan9-ish synthetic filesystems `foo.c/f.{c,dataflow,etc}`. One open question was comfort with more complex visibility transformations or sets - "hide #bar; show 2 levels of headers-only under #hee; ...". Another was cleanup - recognition of "I no longer need this and that".
This is absolutely the hardest bit.
I guess the short-cut is to include all the chat conversation history, and then if the history contains "do X" followed by "no actually do Y instead", then the LLM can figure that out. But isn't it fairly tricky for the agent harness to figure that out, to work out relevancy, and to work out what context to keep? Perhaps this is why the industry defaults to concatenating messages into a conversation stream?
I know you don’t mean it in a reductive sense, but it’s funny /sad that I can imagine
“HTTP is just a way to get a string into a model”
becoming a real piece of wisdom unironically dispensed on this site in the future. Maybe it already is.
Of course Anthropic/OpenAI can do it. And the next day everyone will be complaining how much Claude/Codex has been dumbed down. They don't even comply to the context anymore!
You can always launch a subagent with a fresh context. There are further things that you could do by tweaking the underlying transformer model (such as "joining" any number of independently cached contexts together on an equal basis, without having to rerun prefill on the "later" contexts) but this is quite general already.
Or maybe they haven't thought about it?
Or they tried some simple alternatives and didn't find clear benefits?
> The key is to give the agent not just the ability to pull things into context, but also remove from it.
But then you need rules to figure out what to remove. Which probably involves feeding the whole thing to a(nother?) model anyway, to do that fuzzy heuristic judgment of what's important and what's a distraction. And simply removing messages doesn't add any structure, you still just have a sequence of whatever remains.
Three persistent Claude instances share AMQ with an additional Memory Index to query with an embedding model (that I'm literally upgrading to Voyage 4 nano as I type). It's working well so far, I have an instance Wren "alive" and functioning very well for 12 days going, swapping in-and-out of context from the MCP without relying on any of Anthropic's tools.
And it's on a cheap LXC, 8GB of RAM, N97.
I just make stuff to share with others, so yeah, good point.
When a model is trained on multi-contexts, some growing over time like we see now (conversations), some rolling at various sizes (as in, always on), such as a clock, video feed, audio feed, data streams, tool calling, we no longer have to 'pollute' the main context with a bunch of repetitive data.
But this is going in the direction of 1agent=1mind. When much more likely human (and maybe all cognition) requires 'ghosts' and sub processes. It is much more likely an agent is more like a configurable building piece to a(n alien) mind.
Maybe there’s a way to play around with this idea in pi. I’ll dig into it.
Let's say that you have two agents running concurrently: A & B. Agent A decides to push a message into the context of agent B. It does that and the message ends up somewhere in the list of the message right at the bottom of the conversation.
The question is, will agent B register that a new message was inserted and will it act on it?
If you do this experiment you will find out that this architecture does not work very well. New messages that are recent but not the latest have little effect for interactive session. In other words, Agent A will not respond and say, "and btw, this and that happened" unless perhaps instructed very rigidly or perhaps if there is some other instrumentation in place.
Your mileage may vary depending on the model.
A better architecture is pull-based. In other words, the agent has tools to query any pending messages. That way whatever needs to be communicated is immediately visible as those are right at the bottom of the context so agents can pay attention to them.
An agent in that case slightly more rigid in a sense that the loop needs to orchestrate and surface information and there is certainly not one-size-fits-all solution here.
I hope this helps. We've learned this the hard way.
So hooks are your friends. I also use one as a pre flight status check so it doesn't waste time spinning forever when the API has issues.
Yes you can - durable objects do exactly what the "Ably pub/sub channel transport" diagram describes. And it's even easier with the cloudflare agents SDK. This article strawmans the capabilities of competing infra.
This means:
- less and less "man-in-the-loop"
- less and less interaction between LLMs and humans
- more and more automation
- more and more decision-making autonomy for agents
- more and more risk (i.e., LLMs' responsibility)
- less and less human responsibility
Problem:
Tasks that require continuous iteration and shared decision-making with humans have two possible options:
- either they stall until human input
- or they decide autonomously at our risk
Unfortunately, automation comes at a cost: RISK.
Why do you think the same will not also be true for AI steerers/managers/CEO?
In a year of two, having a human in the loop, will all of their biases and inconsistencies will be considered risky and irresponsible.
But maybe not that much longer; METR task length improvement is still straight lines on log graphs.
Unless your CEO is Steve Jobs, it's hard to imagine it being much worse than your average pointy haired boss.
From which company? I hope you say "Waymo", because Tesla is lying through its teeth and hiding crash statistics from regulators.
I personally believe widely available self-driving cars which don't operate at a loss will continue to elude us until we accept the tradeoffs of dedicated lanes, a standardized vehicle-to-vehicle communication protocol, and roadside sensors. We were lied to.
https://developers.openai.com/api/docs/guides/websocket-mode
I have been building on it over the past month holding WebSocket sessions on workers warm, and command routing using NATS JetStream. With this, it has made using sidecar threads for a main thread very simple, as the worker treats them similar.
If you think about it, about 30% of the biggest businesses out there are based on this exact business idea. IRC - Slack, XMPP & co - the many proprietary messengers out there, etc.
I still sit and watch my terminals. It's the easiest way to catch problems.
The article is about how agents are getting more and more async features, because that's what makes them useful and interesting. And how the standard HTTP based SSE streaming of response tokens is hard to make work when agents are async.
Once I hashed canonical input JSON, cache hit rate on real traffic was higher than expected — mid-teens % once a handful of workers were live. Curious if anyone here's tried cross-agent result sharing without bolting on a full pub/sub layer.
Even if I can string it together it's pretty fragile.
That said I don't really want to solve this with a SaaS. Trying really hard to keep external reliance to a minimum (mostly the llm endpoint)
I vibe coded a message system where I still have all the chat windows open but my agents run a command that finished once a message meant for them comes along and then they need to start it back up again themselves. I kept it semi-automatic like that because I'm still experimenting whether this is what I want.
But they get plenty done without me this way.
I don't think it solves the other half of the problem that we've been working on, which is what happens if you were not the one initiating the work, and therefore can't "connect back into a session" since the session was triggered by the agent in the first place.
Of course the hard bit then is; how does the client know there's new information from the agent, or a new session?
Generally we'd recommend having a separate kind of 'notification' or 'control' pub/sub channel that clients always subscribe to to be notified of new 'sessions'. Then they can subscribe to the new session based purely on knowing the session name.
As an aside, I've built and deployed a production system in which disconnecting & reconnecting from an in-progress LLM stream works and resumes from wherever the stream currently is, through a combination of redis/valkey & websockets - it's not all that hard, it turns out!
The only place I use async now is when I am stepping away and there are a bunch of longer tasks on my plate. So i kick them off and then get to review them when ever I login next. However I dont use this pattern all that much and even then I am not sure if the context switching whenever I get back is really worth it.
Unless the agents get more reliable on long horizon tasks, it seems that async will have limited utility. But can easily see this going into videos feeding the twitter ai launch hype train.
The pattern I describe in the article of 'channels' works really well for one of the hardest bits of using a durable execution tool like Temporal. If your workflow step is long running, or async, it's often hard to 'signal' the result of the step out to some frontend client. But using channels or sessions like in the article it becomes super easy because you can write the result to the channel and it's sent in realtime to the subscribed client. No HTTP polling for results, or anything like that.
> So how are folks solving this?
$5 per month dedicated server, SSH, tmux.
Maybe better somebody standardize that because we'll end up with agents sending rich payloads between themselves via telegram.
Having long living requests, where you submit one, you get back a request_id, and then you can poll for it's status is a 20 year old solved problem.
Why is this such a difficult thing to do in practice for chat apps? Do we need ASI to solve this problem?
If you look at the gifs of the Claude UI in this post[1], you can see how the HTTP response is broken on page refresh, but some time later the full response is available again because it's now being served 'in full' from the database.
[1]: https://zknill.io/posts/chatbots-worst-enemy-is-page-refresh...
Obviously polling works, it's used in lots of systems. But I guess I am arguing that we can do better than polling, both in terms of user experience, and the complexity of what you have to build to make it work.
If your long running operations just have a single simple output, then polling for them might be a great solution. But streaming LLM responses (by nature of being made up of lots of individual tokens) makes the polling design a bit more gross than it really needs to be. Which is where the idea of 'sessions' comes in.
